Fig. 2: Deep learning (DL) workflow. | npj Digital Medicine

Fig. 2: Deep learning (DL) workflow.

From: Deep learning on routine full-breast mammograms enhances lymph node metastasis prediction in early breast cancer

Fig. 2

The study employed a three-step DL approach. a SSL was conducted on unlabeled mammograms to pretrain the backbone feature extractor by maximizing the consistency between augmentations of the same patch. Contiguous patches were tiled from high-resolution mammograms to enable the extraction of subtle variations in local breast tissue. b SL was performed on mammograms of all views with five cancer outcomes to establish a neck module—a global feature extractor, on top of the backbone. For the neck module, a residual block and a vision Transformer were separately evaluated on the ROI or full-breast mammograms. Tumor ROIs were annotated by an automatic detecting tool. The five cancer outcomes investigated included LNM, number of LNMs, LVI, Tsize, and multifocality. c The extracted mammogram features and 11 preoperative clinical variables were concatenated to collaboratively train the final LNM classifier. Mammogram preprocessing details are provided in Supplementary Section 4.

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